scenario-kd-scr-ner-half_data-univner_full44

This model is a fine-tuned version of haryoaw/scenario-TCR-NER_data-univner_full on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0881
• Precision: 0.6065
• Recall: 0.5481
• F1: 0.5758
• Accuracy: 0.9594

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 44
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
2.8487	0.2910	500	2.5495	0.2969	0.0180	0.0340	0.9243
2.158	0.5821	1000	2.1690	0.2565	0.1146	0.1584	0.9273
1.9133	0.8731	1500	2.1064	0.2408	0.1652	0.1960	0.9275
1.7757	1.1641	2000	1.9315	0.2880	0.1886	0.2279	0.9319
1.6744	1.4552	2500	1.8567	0.3126	0.2055	0.2480	0.9340
1.5971	1.7462	3000	1.8138	0.3378	0.2627	0.2956	0.9377
1.5019	2.0373	3500	1.7037	0.3467	0.2767	0.3078	0.9390
1.3519	2.3283	4000	1.6582	0.3893	0.3186	0.3504	0.9403
1.3481	2.6193	4500	1.6593	0.3725	0.3350	0.3528	0.9399
1.3011	2.9104	5000	1.5979	0.4068	0.3300	0.3644	0.9417
1.2064	3.2014	5500	1.5394	0.4172	0.3637	0.3887	0.9443
1.1639	3.4924	6000	1.5140	0.4106	0.3982	0.4043	0.9437
1.1121	3.7835	6500	1.4630	0.4323	0.3763	0.4023	0.9455
1.0805	4.0745	7000	1.4722	0.4025	0.4082	0.4053	0.9459
1.0035	4.3655	7500	1.4320	0.4758	0.3955	0.4319	0.9473
0.9826	4.6566	8000	1.4489	0.4523	0.4275	0.4395	0.9476
0.9617	4.9476	8500	1.3995	0.4649	0.4294	0.4464	0.9496
0.883	5.2386	9000	1.3944	0.4579	0.4477	0.4528	0.9489
0.8619	5.5297	9500	1.3635	0.4842	0.4432	0.4628	0.9500
0.8551	5.8207	10000	1.3569	0.4830	0.4489	0.4653	0.9504
0.8206	6.1118	10500	1.3573	0.4647	0.4613	0.4630	0.9512
0.7659	6.4028	11000	1.3415	0.4865	0.4460	0.4653	0.9518
0.7565	6.6938	11500	1.3367	0.4821	0.4464	0.4636	0.9515
0.7449	6.9849	12000	1.3177	0.4976	0.4666	0.4816	0.9524
0.6966	7.2759	12500	1.3129	0.5167	0.4532	0.4829	0.9525
0.6766	7.5669	13000	1.3272	0.4902	0.4667	0.4782	0.9529
0.675	7.8580	13500	1.2944	0.5046	0.4777	0.4908	0.9529
0.6374	8.1490	14000	1.3035	0.5464	0.4540	0.4959	0.9535
0.617	8.4400	14500	1.2621	0.5113	0.5136	0.5125	0.9537
0.6172	8.7311	15000	1.2523	0.5155	0.5158	0.5156	0.9546
0.6023	9.0221	15500	1.2635	0.5292	0.4992	0.5138	0.9549
0.5671	9.3132	16000	1.2415	0.5143	0.5223	0.5183	0.9552
0.5602	9.6042	16500	1.2467	0.5408	0.5005	0.5199	0.9560
0.5565	9.8952	17000	1.2424	0.5378	0.5092	0.5231	0.9558
0.5292	10.1863	17500	1.2431	0.5391	0.5184	0.5285	0.9559
0.5152	10.4773	18000	1.2265	0.5334	0.5377	0.5356	0.9560
0.5217	10.7683	18500	1.2145	0.5490	0.5168	0.5324	0.9565
0.4982	11.0594	19000	1.2149	0.5832	0.5064	0.5421	0.9569
0.4801	11.3504	19500	1.2300	0.5349	0.5190	0.5268	0.9567
0.4723	11.6414	20000	1.2026	0.5545	0.5237	0.5387	0.9568
0.4768	11.9325	20500	1.2123	0.5735	0.5165	0.5435	0.9567
0.4573	12.2235	21000	1.1930	0.5904	0.5116	0.5482	0.9576
0.4447	12.5146	21500	1.2093	0.5541	0.5419	0.5480	0.9574
0.451	12.8056	22000	1.2137	0.5457	0.5337	0.5396	0.9568
0.4403	13.0966	22500	1.2029	0.5715	0.5100	0.5390	0.9571
0.4258	13.3877	23000	1.1842	0.5790	0.5389	0.5582	0.9575
0.4204	13.6787	23500	1.1901	0.5654	0.5178	0.5406	0.9571
0.4195	13.9697	24000	1.1973	0.5785	0.5053	0.5394	0.9571
0.3989	14.2608	24500	1.1863	0.5751	0.5312	0.5523	0.9579
0.3989	14.5518	25000	1.1764	0.5652	0.5460	0.5554	0.9575
0.4004	14.8428	25500	1.1896	0.6038	0.5041	0.5495	0.9580
0.3908	15.1339	26000	1.1819	0.5926	0.5210	0.5545	0.9580
0.3825	15.4249	26500	1.1591	0.5820	0.5354	0.5578	0.9581
0.377	15.7159	27000	1.1720	0.5723	0.5350	0.5530	0.9575
0.374	16.0070	27500	1.1424	0.5729	0.5406	0.5563	0.9583
0.3591	16.2980	28000	1.1840	0.5565	0.5498	0.5532	0.9577
0.3608	16.5891	28500	1.1557	0.5829	0.5431	0.5623	0.9581
0.3636	16.8801	29000	1.1718	0.5880	0.5275	0.5561	0.9580
0.3547	17.1711	29500	1.1445	0.5751	0.5497	0.5621	0.9582
0.3468	17.4622	30000	1.1362	0.5938	0.5324	0.5614	0.9588
0.3444	17.7532	30500	1.1412	0.5984	0.5470	0.5715	0.9590
0.3379	18.0442	31000	1.1374	0.5836	0.5445	0.5634	0.9585
0.3334	18.3353	31500	1.1453	0.5808	0.5292	0.5538	0.9586
0.3335	18.6263	32000	1.1363	0.5843	0.5485	0.5659	0.9588
0.33	18.9173	32500	1.1517	0.5939	0.5451	0.5685	0.9588
0.3262	19.2084	33000	1.1323	0.6000	0.5278	0.5616	0.9585
0.3233	19.4994	33500	1.1322	0.5906	0.5380	0.5631	0.9587
0.3188	19.7905	34000	1.1306	0.5869	0.5527	0.5693	0.9587
0.3164	20.0815	34500	1.1411	0.5946	0.5296	0.5602	0.9585
0.3137	20.3725	35000	1.1301	0.5960	0.5428	0.5681	0.9587
0.3085	20.6636	35500	1.1298	0.5890	0.5337	0.5600	0.9586
0.3105	20.9546	36000	1.1364	0.5733	0.5442	0.5584	0.9588
0.3047	21.2456	36500	1.1228	0.6020	0.5457	0.5725	0.9594
0.2971	21.5367	37000	1.1253	0.6087	0.5380	0.5712	0.9591
0.303	21.8277	37500	1.1135	0.6096	0.5460	0.5760	0.9592
0.3018	22.1187	38000	1.1254	0.6101	0.5449	0.5757	0.9590
0.2906	22.4098	38500	1.1173	0.5975	0.5543	0.5751	0.9592
0.2931	22.7008	39000	1.1113	0.5997	0.5432	0.5701	0.9592
0.2934	22.9919	39500	1.1233	0.6080	0.5428	0.5736	0.9591
0.2885	23.2829	40000	1.1213	0.6074	0.5480	0.5762	0.9590
0.2839	23.5739	40500	1.1122	0.6030	0.5403	0.5699	0.9590
0.2892	23.8650	41000	1.1152	0.5958	0.5396	0.5663	0.9584
0.2836	24.1560	41500	1.1141	0.6037	0.5467	0.5738	0.9593
0.2801	24.4470	42000	1.1061	0.5908	0.5519	0.5707	0.9592
0.2817	24.7381	42500	1.1044	0.6056	0.5503	0.5766	0.9591
0.2814	25.0291	43000	1.1027	0.6142	0.5491	0.5798	0.9597
0.2775	25.3201	43500	1.1090	0.6068	0.5438	0.5736	0.9591
0.2778	25.6112	44000	1.1048	0.6068	0.5491	0.5765	0.9597
0.2708	25.9022	44500	1.1111	0.6030	0.5467	0.5734	0.9590
0.2755	26.1932	45000	1.1018	0.6089	0.5481	0.5769	0.9595
0.2698	26.4843	45500	1.1116	0.6023	0.5305	0.5641	0.9588
0.2699	26.7753	46000	1.0993	0.6102	0.5421	0.5741	0.9595
0.2717	27.0664	46500	1.0905	0.6031	0.5425	0.5712	0.9590
0.2657	27.3574	47000	1.0948	0.6024	0.5586	0.5797	0.9598
0.2675	27.6484	47500	1.0910	0.6159	0.5501	0.5812	0.9596
0.2676	27.9395	48000	1.0930	0.6018	0.5484	0.5739	0.9596
0.2652	28.2305	48500	1.0991	0.6024	0.5480	0.5739	0.9591
0.2637	28.5215	49000	1.0981	0.6058	0.5468	0.5748	0.9594
0.2656	28.8126	49500	1.0988	0.6060	0.5397	0.5710	0.9594
0.2628	29.1036	50000	1.0986	0.6094	0.5510	0.5787	0.9597
0.2622	29.3946	50500	1.0884	0.6079	0.5465	0.5756	0.9598
0.2602	29.6857	51000	1.0995	0.6065	0.5400	0.5714	0.9594
0.2646	29.9767	51500	1.0881	0.6065	0.5481	0.5758	0.9594

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.19.1